PROJECT SUMMARY/ABSTRACT Myosins are a diverse and ubiquitous class of molecular motors that are responsible for generating much of the macroscopic force in the human body. The human genome encodes 38 different isoforms of myosin, and members of this group act as force sensors or generators for a diverse set of processes throughout the body. To serve this wide array of functions, each myosin isoform has been biophysically tuned for its physiological role. In fact, the tuning is so precise that missense variants in one myosin isoform, !-cardiac myosin, can cause a congenital cardiomyopathy that is the leading cause of sudden cardiac death in people under 30. And yet, it is unknown how particular variants cause disease, or how to infer the pathogenic potential for novel mutations. Large differences in functional properties between myosin isoforms are not the result of large differences in coding sequence or overall topology. Neither foreknowledge of phylogeny nor crystal structure is suf?cient to predict an isoform's biophysical properties. Furthermore, mutations causing disease frequently occur in regions of the protein far from the site of their deleterious effects. Poor understanding of the biophysical regulation of motor function has hampered the development of pharmaceuticals and the interpretation of human genomic data. My goal is to establish a mechanistic understanding of myosin motors that is capable of predicting if and how sequence variation changes biophysical properties and can cause cardiac disease. Since myosin kinetics are not apparent from sequence or overall structure, they must be determined by other factors. I hypothesize that kinetic differences result from differences in the allosteric networks in these proteins. Allosteric network in this context refers to the coordinated conformational ?uctuations that give protein regulation the appearance of action at a distance. To test this hypothesis, we will use our unique combination of enormous computational power for molecular simulation and cutting-edge machine learning tools for analyzing protein allostery. Aim 1 is to identify the biophysical determinants of myosin isoforms' differing speeds. To test our hypothesis that allosteric networks are responsible for modulating dynamics, I will use molecular simulations of different myosin isoforms and compare their allosteric networks with biochemical data about their properties. Aim 1 directly addresses outstanding questions about normal molecular-biological function of the heart, putting it in line with NHLBI overarching objective #1. Aim 2 is to determine the difference, at atomic resolution, between healthy and diseased !-cardiac myosin. I hypothesize that the pathogenicity of variants with an unknown molecular etiology is a consequence of allosteric disruption, and will use our computational tools to test this hypothesis by simulating a set of known-pathogenic variants. This aim uses techniques from data science to understand the genetic determinants of health, and will apply equally well to rare alleles in under-represented groups as to majority groups. It is directly addresses NHLBI overarching objectives #3, #4, and #7.